The RAG Experiment Accelerator is a versatile tool designed to expedite and facilitate the process of conducting experiments and evaluations using Azure Cognitive Search and RAG pattern.
checkpoint and resume an iterable dataset (e.g. when streaming):
>>> iterable_dataset = Dataset.from_dict({"a": range(6)}).to_iterable_dataset(num_shards=3)
>>> for idx, example in enumerate(iterable_dataset):
... print(example)
... if idx == 2:
... state_dict = iterable_dataset.state_dict()
... print("checkpoint")
... break
>>> iterable_dataset.load_state_dict(state_dict)
>>> print(f"restart from checkpoint")
>>> for example in iterable_dataset:
... print(example)
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Bumps datasets from 2.19.2 to 2.20.0.
Release notes
Sourced from datasets's releases.
... (truncated)
Commits
98fdc9e
Release: 2.20.0 (#6969)af3acfd
fix(ci): remove unnecessary permissions (#6962)37a6036
Move info_utils errors to exceptions module (#6952)9510252
Better error handling indataset_module_factory
(#6959)97513be
feat(ci): add trufflehog secrets detection (#6960)686f5df
Add support for categorical/dictionary types (#6892)a2dc287
Remove defaulttrust_remote_code=True
(#6954)09ebf51
Validate config name and data_files in packaged modules (#6915)5bbbf1b
Validate config name and data_files in packaged modules (#6915)6548e0e
Fix typos in docs (#6957)Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
@dependabot rebase
.Dependabot commands and options
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